# Core A:  Mouse Core

> **NIH NIH P01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $241,130

## Abstract

STAT3 Variants as a Rheostat of Immune Tolerance Mouse Core
Project Summary
The use of genetically altered mice has revolutionized the study of complex biological processes, including
immune tolerance and autoimmunity. All the projects in this P01 rely heavily on the use of genetically modified
transgenic, knockout, and autoimmune-prone mouse strains. Many autoimmune prone and immunodeficient
lines require specific expertise and conditions for optimal research, including housing in pathogen-free
environments. Additionally, breeding and care of such animals can become complex when dealing with
multiple genetically modified lines and different genetic strains of mice. A mouse Core is envisioned that will
help streamline and improve efficiencies with the generation and maintenance of genetically complex lines in
a healthy, pathogen-free environment. Because of the reliance of several P01 projects on the NOD mouse
model of diabetes and the novel STAT3 gain of function (GOF) mouse lines, much of the Core efforts will
revolve around the maintenance, distribution and use of these lines in combination with other relevant immune
system reporters or immunodeficient lines. In this way, the Core can help ensure the quality, genetic lineage,
and sharing of animals needed for the collaborative studies of the P01 investigators.

## Key facts

- **NIH application ID:** 10328099
- **Project number:** 1P01AI155393-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Mark S Anderson
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $241,130
- **Award type:** 1
- **Project period:** 2022-02-17 → 2027-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10328099

## Citation

> US National Institutes of Health, RePORTER application 10328099, Core A:  Mouse Core (1P01AI155393-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10328099. Licensed CC0.

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